For example, an X-ray chest image has a very broad range of pixel values since it is made up of an image region of lungs through which X-rays are readily transmitted, and an image region of a mediastinal part through which X-rays are hardly transmitted. For this reason, it has been considered to be difficult to obtain an X-ray chest image that allows to simultaneously observe both the lungs and mediastinal part.
As a method of avoiding this problem, a method described in SPIE Vol. 626 Medicine XIV/PACS IV (1986) is known. This method is described using constants A, B, and C (for example, A=3, B=0.7) by:SD=A[Sorg−SUS]+B[SUS]+C  (1)where SD is the pixel value of an image after processing, Sorg is the pixel value (input pixel value) of an original image (input image), and SUS is the pixel value of a low-frequency image of the original image.
This method can change weighting coefficients for high-frequency components (first term) and low-frequency components (second term). For example, when A=3 and B=0.7, the effect of emphasizing the high-frequency components and compressing the overall dynamic range can be obtained. Five radiologists evaluated that this method is effective for diagnosis compared to an image without any processing.
Japanese Patent No. 2509503 describes a method which is described by:SD=Sorg+F[G(Px, Py)]  (2)where SD is the pixel value after processing, Sorg is the original pixel value (input pixel value), Py is the average profile of a plurality of Y-profiles of an original image, and Px is the average profile of a plurality of X-profiles.
The characteristics of the function F(x) will be explained below. If “x>Dth”, F(x) becomes “0”. If “0≦x≦Dth”, F(x) monotonously decreases to have “E” as a segment and “E/Dth” as a slope. F(x) is given by:F(x)=E−(E/Dth)x, when 0≦x≦Dth =0, when x>Dth  (3)Py=(ΣPyi)/n  (4)Px=(ΣPxi)/n  (5)where (i=1 to n), and Pyi and Pxi are profiles. For example, G(Px, Py) is given by:G(Px, Py)=max(Px, Py)  (6)In this method, of the pixel value (density value) range of the original image, the pixel value (density value) range in which the pixel values of a low-frequency image are equal to or smaller than Dth is compressed.
As a method similar to the method of Japanese Patent No. 2509503, a method described in “Anan et. al., Japanese Journal of Radiological Technology, Vol. 45, No. 8, August 1989, p. 1030”, and Japanese Patent No. 2663189 is known. Using the monotone decreasing function f(x), this method is described by:SD=Sorg+f(SUS)  (7)SUS=ΣSorg/M2  (8)where SD is the pixel value after processing, Sorg is the original pixel value, and SUS is the average pixel value upon calculating a moving average using a mask size M×M pixels in the original image.
In this method, the low-frequency image generation method is different from that in the method given by equation (2). In the method given by equation (2), a low-frequency image is generated based on one-dimensional data, while in this method, a low-frequency image is generated based on two-dimensional data. In this method as well, of the pixel value (density value) range of the original image, the pixel value (density value) range in which the pixel values of a low-frequency image are equal to or smaller than Dth is compressed.
The aforementioned dynamic range compression method can be expressed using a function f1( ) of converting (compressing) a low-frequency image by:SD=f1(SUS)+(Sorg−SUS)  (9)Note that the variable of a function may be omitted for the sake of simplicity in this specification.
The dynamic range compression method given by equation (9) will be explained below. FIGS. 1 and 2 are views for explaining the principle of that method. The uppermost view in FIG. 1 shows the profile of an edge portion of an original image, the middle view shows the profile of a smoothed image of that original image, and the lowermost view shows the profile of a high-frequency image generated by subtracting the smoothed image from the original image. In FIG. 2, the uppermost view shows the profile of an image obtained by multiplying by ½ the absolute values of the smoothed image in the middle view of FIG. 1, the middle view shows the same profile as that of the high-frequency image in FIG. 1, and the lowermost view shows the profile of an image obtained by adding the high-frequency image in the interrupt view to the image in the uppermost view obtained by converting the values of the smoothed image. A process for obtaining an image, the dynamic range of which is compressed, like the image shown in the lowermost view in FIG. 2, is called a dynamic range compression process.
In recent years, multiple-frequency processes (to be also referred to as multiple-frequency transformation processes hereinafter) using Laplacian pyramid transformation and wavelet transformation have been developed. In these multiple-frequency processes, a frequency process (a process for emphasizing or suppressing specific spatial frequency components) of an image is implemented by converting Laplacian coefficients or wavelet coefficients obtained by decomposing an image into a plurality of frequency components.